Fusion of depth maps with multiple scales

Fuhrmann, Simon; Goesele, Michael

doi:10.1145/2024156.2024182

Cited by 55 publications

(51 citation statements)

References 32 publications

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“…When a scene is covered with scans at different scales, fine resolution surface details can be lost. Fuhrmann and Goesele [21] developed a hierarchical volume approach to retain the maximum amount of details. However, the method is designed to combine a very high number of viewpoints for off-line processing; as such, it is unsuited to real-time use.…”

Section: Visibility Methodsmentioning

confidence: 99%

Real-time scene reconstruction and triangle mesh generation using multiple RGB-D cameras

Meerits

Nozick

Saitō

2017

J Real-Time Image Proc

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We present a novel 3D reconstruction system that can generate a stable triangle mesh using data from multiple RGB-D sensors in real time for dynamic scenes. The first part of the system uses moving least squares (MLS) point set surfaces to smooth and filter point clouds acquired from RGB-D sensors. The second part of the system generates triangle meshes from point clouds. The whole pipeline is executed on the GPU and is tailored to scale linearly with the size of the input data. Our contributions include changes to the MLS method for improving meshing, a fast triangle mesh generation method and GPU implementations of all parts of the pipeline.

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Section: Visibility Methodsmentioning

confidence: 99%

Real-time scene reconstruction and triangle mesh generation using multiple RGB-D cameras

Meerits

Nozick

Saitō

2017

J Real-Time Image Proc

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“…Common representations for 3D models include point clouds, surfels [10], triangle meshes [27], and signed distance functions [6]. Octrees allow for efficient data storage and fusion at multiple scales [9,24,28]. While KinectFusion [17] demonstrated that impressive 3D models can be acquired by tracking and fusing the depth images directly into a signed distance volume, drift will accumulate in the 3D model and inevitably lead to inconsistencies.…”

Section: Related Workmentioning

confidence: 99%

Submap-Based Bundle Adjustment for 3D Reconstruction from RGB-D Data

Maier¹,

Sturm²,

Cremers³

2014

Lecture Notes in Computer Science

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Abstract. The key contribution of this paper is a novel submapping technique for RGB-D-based bundle adjustment. Our approach significantly speeds up 3D object reconstruction with respect to full bundle adjustment while generating visually compelling 3D models of high metric accuracy. While submapping has been explored previously for mono and stereo cameras, we are the first to transfer and adapt this concept to RGB-D sensors and to provide a detailed analysis of the resulting gain. In our approach, we partition the input data uniformly into submaps to optimize them individually by minimizing the 3D alignment error. Subsequently, we fix the interior variables and optimize only over the separator variables between the submaps. As we demonstrate in this paper, our method reduces the runtime of full bundle adjustment by 32% on average while still being able to deal with real-world noise of cheap commodity sensors. We evaluated our method on a large number of benchmark datasets, and found that we outperform several state-ofthe-art approaches both in terms of speed and accuracy. Furthermore, we present highly accurate 3D reconstructions of various objects to demonstrate the validity of our approach.

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“…However, depth samples across views possessing different scales is challenging for VRIP approaches. One example addressing this issue is the scale space representation presented in (Fuhrmann and Goesele, 2011). They build a multi-level octree holding vertices at different scales.…”

Section: Introductionmentioning

confidence: 99%

“…Similar to (Fuhrmann and Goesele, 2011) we first assign points to an multi-level octree structure according to the pixel footprint. Subsequent filter operations are then carried out on points of the locally lowest tree-levels, whereas high level points are discarded.…”

Section: Introductionmentioning

confidence: 99%

A Median-Based Depthmap Fusion Strategy for the Generation of Oriented Points

Rothermel¹,

Haala²,

Fritsch³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Due to good scalability, systems for image-based dense surface reconstruction often employ stereo or multi-baseline stereo methods. These types of algorithms represent the scene by a set of depth or disparity maps which eventually have to be fused to extract a consistent, non-redundant surface representation. Generally the single depth observations across the maps possess variances in quality. Within the fusion process not only preservation of precision and detail but also density and robustness with respect to outliers are desirable. Being prune to outliers, in this article we propose a local median-based algorithm for the fusion of depth maps eventually representing the scene as a set of oriented points. Paying respect to scalability, points induced by each of the available depth maps are streamed to cubic tiles which then can be filtered in parallel. Arguing that the triangulation uncertainty is larger in the direction of image rays we define these rays as the main filter direction. Within an additional strategy we define the surface normals as the principle direction for median filtering/integration. The presented approach is straight-forward to implement since employing standard oc-and kd-tree structures enhanced by nearest neighbor queries optimized for cylindrical neighborhoods. We show that the presented method in combination with the MVS (Rothermel et al., 2012) produces surfaces comparable to the results of the Middlebury MVS benchmark and favorably compares to an state-of-the-art algorithm employing the Fountain dataset (Strecha et al., 2008). Moreover, we demonstrate its capability of depth map fusion for city scale reconstructions derived from large frame airborne imagery.

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Fusion of depth maps with multiple scales

Cited by 55 publications

References 32 publications

Real-time scene reconstruction and triangle mesh generation using multiple RGB-D cameras

Real-time scene reconstruction and triangle mesh generation using multiple RGB-D cameras

Submap-Based Bundle Adjustment for 3D Reconstruction from RGB-D Data

A Median-Based Depthmap Fusion Strategy for the Generation of Oriented Points

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